Abstract: For continuous domains rough sets attribute decision table is not easy to deal with poor ability to obtain the relationship between fuzzy sets and other issues, propose a fuzzy sets and rough sets combine continuous condition attribute reduction algorithm of fuzzy rules of the Firstly, the introduction of triangular membership function continuous attribute values into fuzzy values, and the use of discrete fuzzy neural network method to obtain the relationship between data sets. instance verification shows that this algorithm can be based on the actual decision-making needs of users and domain knowledge to change the thresholds, fuzzy rules to obtain a satisfactory result.

Keywords: condition attribute; continuous; membership function; fuzzy rules

Abstract: To solve the problems of low adaptability for continuous domain reduction and the disadvantage of failing to obtain eventual relationship among the fuzzy sets, this paper proposed a new method of attribute reduction algorithms of decision table based on combining fuzzy set with rough set. First , transformed continuous attribute value into fuzzy value with triangular membership function, then provided algorithms of hard C-means (HCM) clustering to obtain relationship among the fuzzy sets.In the end, simulation results show the effectiveness of the proposed method through an illustrative example .

Fuzzy set theory is a kind used in modeling the experimental data for some of the uncertainty and ambiguity issues a powerful tool of its advantages: fuzzy set theory provides a systematic, language computing tools represent such information, expressed by the membership function by using linguistic variables, it can be calculated. reasonable choice of fuzzy rules is a key factor in the fuzzy inference system, which can effectively for specific applications in the field of human expertise modeled. Pawlak rough set point theory and fuzzy set theory are not mutually exclusive but can complement each other [3]; Dubois et al [4] further indicated that they are uncertain knowledge processing two mathematical methods are complementary in nature. To this end, This paper proposes a rough sets and fuzzy sets combine continuous condition attribute reduction algorithm of fuzzy rules.

Definition 2 For continuous domain decision table S = <U,C,D,V,f>, object u? I and u? S in continuous attribute c? J similarity is defined as follows:

Definition 3 For continuous domain decision table S = <U,C,D,V,f>, object u? I in a continuous-type attribute c? J on a similar class defined as follows:

Definition 4 For continuous domain decision table S = <U,C,D,V,f>, continuous attribute c? J divided on U formed clusters consisting of similar vectors are defined as follows:

Definition 5 For continuous domain decision table S = <U,C,D,V,f>, assuming continuous attribute c? I on U division formed similar clusters composed of vector is defined as simClassVector (c? J) = (sim? βc?? j (u? i) | i = 1,2, ..., n), then the continuous attribute c? i feature vector is defined as the number

rij = 1-δ ×? nk = 1 | λik-λjk | (6) ** Where: i, j = 1,2, .**.., m; 0 <δ <1 is a constant; m is the total number of condition attributes.

Input: continuous domain decision table S = <U,C,D,V,f>, similarity threshold β, similar to the matrix element constant coefficient δ, fuzzy equivalent matrix cut set threshold λ.

sim?? 0.8c?? 1 (u? 2) = {u? 2, u? 4, u? 5}

sim?? 0.8c?? 1 (u? 3) = {u? 3, u? 5, u? 6}

sim?? 0.8c?? 1 (u? 4) = {u? 2, u? 4}

sim?? 0.8c?? 1 (u? 5) = {u? 2, u? 3, u? 5, u? 6}

sim?? 0.8c?? 1 (u? 2) = {u? 3, u? 5, u? 6}

Through this example illustrates the use of the algorithm can not only solve the continuous domain decision table attribute reduction issues, but also may need to obtain a subjective attribute reduction set and a set of fuzzy rule sets, which illustrate the algorithm is feasible.

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